Who Gets Watched? Allocating Scarce Escalation Resources on Pediatric Wards with Neural Index Policies

Title:Who Gets Watched? Allocating Scarce Escalation Resources on Pediatric Wards with Neural Index Policies

Abstract:Problem definition: Hospitals use separate pathways to identify and respond to deteriorating ward patients. At Children's Healthcare of Atlanta (CHOA), these include Watcher surveillance, Critical Care Outreach Team assessment, and transfer to the intensive care unit. When several patients need attention simultaneously, however, no common process determines who should receive which response under limited staffing and capacity. We study how to coordinate these actions to support earlier escalation without unnecessary monitoring. Methodology/results: We model ward escalation decisions using a multi-action restless multi-armed bandit model and develop Multi-action Index Learning and Optimization Policy (MILOP) as a computational solution approach. MILOP trains a neural network to generate a high-quality index policy using a decision-focused learning approach. We train and evaluate MILOP using historical CHOA data and compare it with two practical baselines. MILOP recommends an escalation before 87.4% of critical or emergent intensive-care transfers, compared with 79.5% for the stronger baseline, while using 13.5% less monitoring resources. MILOP provides a median of 19.2 hours lead time before transfer, earlier than either baseline. Moreover, 99.3% of MILOP's proactive transfer recommendations are preceded by Watcher surveillance or outreach-team assessment, demonstrating a desirable escalation of care. Managerial implications: MILOP can help leaders focus scarce clinical attention, gain more time to respond before severe deterioration, and reduce monitoring burden compared to existing practices. Broadly, hospitals may improve escalation decisions by coordinating existing pathways rather than managing each separately or using risk scores as stand-alone decision rules.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2510.22069 [cs.LG]

(or arXiv:2510.22069v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2510.22069 Focus to learn more arXiv-issued DOI via DataCite

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